octave-a4.pdf.tar

本程序可绘制无码间串扰的眼图，可设置显示不同的升余弦滚降系数以及眼图个数！希望对大家有用！(This program can be drawn Uncensored crosstalk eye-diagram, you can set different raised cosine roll-off factor and the number of eye diagram! I hope useful for all of us!)

matlab中文帮助文档,MATLAB教程,初学者必看的文档。(Chinese help documentation matlab, MATLAB tutorial, beginners must see the document)

Steganography using matlab

对离散时间信号进行傅里叶变换，得到傅里叶变换后的图样(Discrete-time signal pattern of the Fourier transform, the Fourier-transformed)

在优化排样中进行matlab的绘图，输入各边长宽及个数后自动生成图形(pictrue used for stock layout)

说明：  这是一个深度神经网络LSTM的教程，里面包含许多例子(Example of Lstm network)

合并两幅图像为一副图像。ImgMatch.m 为主文件。采用的是兴趣点合并。(Merge the two images as an image based on interest point. ImgMatch.m is the main file. )

matlab的教程，有简易版和详细版。较为浅显易懂，对于新手来说，能够较快的入门。(matlab tutorial, there are simple and detailed version version. Relatively easy to understand for the novice is able to fast entry.)

《统计与自适应信号处理》是国外最新出版的一本统计信号处理教材。其主要特点是对统计与自适应信号处理的理论方法、实现及应用给出了较完整的论述和介绍。作者根据其多年的教学经验，并针对现代信号处理的理论及应用方面的重要性，选定谱估计、信号建模、自适应滤波与阵列信号处理等几个关键论题进行了详细的讨论。《统计与自适应信号处理》强调基本概念和理论方法，目的是为读者以后深入研究一些信号处理的新课题打下良好的基础。为了便于掌握，书中的数学描述和推导仅限于高年级本科生、研究生和工程技术人员能够理解的水平，因此《统计与自适应信号处理》具有较强的实用性。 《统计与自适应信号处理》适用于大学高年级本科生和研究生作为选修现代信号处理课程的教学参考书，也可作为工程技术人员自修现代信号处理理论及应用的参考书("Statistical and Adaptive Signal Processing" is the latest edition of a foreign statistical signal processing materials. Its main characteristic is the statistical theory and adaptive signal processing methods, and applications are given to achieve a more complete discussion and description. According to their years of teaching experience, and for modern signal processing theory and application of the importance of the selected spectral estimation, signal modeling, adaptive filtering and array signal processing, and several other key topics were discussed in detail. "Statistical and Adaptive Signal Processing," emphasizing basic concepts and theoretical methods, the reader is intended to signal processing after the in-depth study some new issues and lay a good foundation. In order to facilitate control, the mathematical description and derivation of the book is limited to advanced undergraduates, graduate students and engineers to understand the levels, "Statistical and Adaptive Signal)

分形的练习一 ①Koch曲线 用复数的方法来迭代Koch曲线 clear i 防止i被重新赋值 A=[0 1] 初始A是连接（0，0）与（1，0）的线段 t=exp(i*pi/3) n=2 n是迭代次数 for j=0:n A=A/3 a=ones(1,2*4^j) A=[A (t*A+a/3) (A/t+(1/2+sqrt(3)/6*i)*a) A+2/3*a] end plot(real(A),imag(A)) axis([0 1 -0.1 0.8]) ②Sierpinski三角形 A=[0 1 0.5 0 0 1] 初始化A n=3 迭代次数 for i=1:n A=A/2 b=zeros(1,3^i) c=ones(1,3^i)/2 A=[A A+[c b] A+[c/2 c]] end for i=1:3^n patch(A(1,3*i-2:3*i),A(2,3*i-2:3*i), b ) patch填充函数 end (Fractal Exercise One The ① Koch curve Plural iteration Koch curve clear i to prevent i is reassigned A = [0 1] initial A is a connection (0,0) and (1,0) of the segments t = exp (i* pi/3) n = 2 n is the number of iterations for j = 0: n A = A/3 a = ones (1,2* 4 ^ j) A = [A (t* A+ a/3) (A/t+ (1/2+ sqrt (3)/6* i)* a) A+2/3* a] end plot (real (A), imag (A)) axis ([0 1-0.1 0.8])   ② Sierpinski triangle A = [0 1 0.5 0 0 1] initialized A n = 3 the number of iterations. for i = 1: n A = A/2 b = zeros (1,3 ^ i) c = ones (1,3 ^ i)/2 A = [A A+ [c b] A+ [c/2 c]] end for i = 1:3 ^ n patch (A (1,3* i-2: 3* i), A (2,3* i-2: 3* i), b ) patch filled function end)